The Research Of Moving Object Tracking Based On Artificial Fish Swarm Algorithm

The particle filter method is an effective algorithm which is put forward for target tracking. Due to the degeneration phenomenon in the algorithm of particle filter, state estimate to a trajectory may be invalid, resource is wasted, and performance is reduced. Therefore, the method with real-time, accuracy and robustness is a key for the target tracking. The abilities of the maximum drifting and jumping out of a local extreme value are possessed by the algorithm of artificial fish swarm. So, the algorithm of artificial fish swarm has an important application value in the area of target tracking. Target tracking using the algorithm of artificial fish swarm is studied in the paper.Based on the analysis of the algorithms of artificial fish and particle filter, the target tracking model by artificial fish swarm is proposed in the paper. The behaviors of preying, clustering, tailgating in artificial fish swarm are adopted. Different behavior is defined as different state transition formula which is selected to use according to the distance near a target. Thus, the same transition formula in classical particle filter is avoided and this makes particles more intelligent. Simulation results show that the algorithm can be used to solve the losing target phenomenon when a motion target speeding and changing direction suddenly or being blocked. A new method is proposed in target tracking model.In order to expand distribution scope and quickly get a global optimal state, the evolution of mutation operator is used to increase the diversity of artificial fish in the paper. Based on the technology of synergistic control, artificial fish is divided into different groups in distribution system. In different groups three behaviors are tested and the better value is gained which is used to guide the motion of other groups. The cooperation and mutual information between any individual and groups are realized. At the same time, the aimless random swimming of artificial fish is eliminated. Simulation results show that the target moving randomly and dynamically can be tracked, the search scope expanded, and tracking performance improved by the proposed method.